import pandas as pd
import urllib
import numpy as np
import urllib.request
import re
from textblob import TextBlob
%run lib.py
#name="Legally%20Blonde"
#name="aboutmary"
#name="10Things"
name="magnolia"
#name="Friday%20The%2013th"
#name="Ghost%20Ship"
#name="Juno"
#name="Reservoir+Dogs"
#name="shawshank"
#name="Sixth%20Sense,%20The"
#name="sunset_bld_3_21_49"
#name="Titanic"
#name="toy_story"
#name="trainspotting"
#name="transformers"
#name="the-truman-show_shooting"
#name="batman_production"
ext="html"
txtfiles=["Ghost%20Ship", "Legally%20Blonde", "Friday%20The%2013th", "Juno", "Reservoir+Dogs", "Sixth%20Sense,%20The", "Titanic"]
if name in txtfiles:
ext="txt"
fp = urllib.request.urlopen("http://www.dailyscript.com/scripts/"+name+"."+ext)
mybytes = fp.read()
mystr = mybytes.decode("utf8", "ignore")
fp.close()
liston=mystr.split("\n")
liston=[s.replace('\r', '') for s in liston]
liston=[re.sub('<[^<]+?>', '', text) for text in liston]
if name=="shawshank":
liston=[i.replace("\t", " ") for i in liston]
char=""
script=[]
charintro=' '
endofdialogue=' '
dialoguepre=' '
newscenepre=' '
charintro=''
endofdialogue=''
dialoguepre=''
newscenepre=''
i=45
print("Characters")
i, charintro=nextbigchunk(liston, i)
print("Adverbs")
i, adverb=nextbigchunk(liston, i, adverbs=True)
print("Dialogues")
i, dialoguepre=nextbigchunk(liston, i)
print("New Scene:")
i, newscenepre=nextbigchunk(liston, i)
if newscenepre=="X":
i=100
i, newscenepre=nextbigchunk(liston, i)
if name=="aboutmary":
newscenepre=" ".join(["" for i in range(56)])
if len(newscenepre)==len(charintro):
newscenepre="X"
endofdialogue=newscenepre
scene=1
for s in liston:
if s[0:len(charintro)]==charintro and s[len(charintro)]!=" " and s.strip()[0]!="(" and s.strip()[len(s.strip())-1]!=")":
#print("Charatcer*****")
char=s[len(charintro):]
new=dict()
new['char']=char.strip()
new['dialogue']=""
new['scene']=scene
new['adverb']=""
if s==endofdialogue or s.replace(" ", "")=="":
if char!="":
char=""
script.append(new)
if char!="" and s[0:len(dialoguepre)]==dialoguepre and s[len(dialoguepre)]!=" ":
#print("Dialogue******")
if new['dialogue']!="":
new['dialogue']=new['dialogue']+" "
new['dialogue']=new['dialogue']+s[len(dialoguepre):]
if char!="" and ((s[0:len(adverb)]==adverb and s[len(adverb)]!=" ") or (len(s)>1 and s.strip()[0]=="(" and s.strip()[len(s.strip())-1]==")" )):
if new['adverb']!="":
new['adverb']=new['adverb']+" "
new['adverb']=new['adverb']+s[len(adverb):]
if s[0:len(newscenepre)]==newscenepre and len(s)>len(newscenepre) and ( s.isupper()) and s[len(newscenepre)]!=" ":
scene=scene+1
pd.DataFrame(script).to_csv(name+'.csv', index=None)
pd.DataFrame(script)
magnolia=pd.read_csv(name+'.csv')
stopwords = getstopwords()
removedchars=["'S VOICE", "'S WHISPER VOICE", " GATOR"]
for s in removedchars:
magnolia['char']=magnolia['char'].apply(lambda x: x.replace(s, ""))
i=0
scenes=dict()
for s in magnolia.iterrows():
scenes[s[1]['scene']]=[]
for s in magnolia.iterrows():
scenes[s[1]['scene']].append(s[1]['char'])
for s in magnolia.iterrows():
scenes[s[1]['scene']]=list(set(scenes[s[1]['scene']]))
characters=[]
for s in scenes:
for k in scenes[s]:
characters.append(k)
characters=list(set(characters))
appearances=dict()
for s in characters:
appearances[s]=0
for s in magnolia.iterrows():
appearances[s[1]['char']]=appearances[s[1]['char']]+1
a=pd.DataFrame(appearances, index=[i for i in range(len(appearances))])
finalcharacters=[]
for s in pd.DataFrame(a.transpose()[0].sort_values(0, ascending=False))[0:10].iterrows():
finalcharacters.append(s[0])
finalcharacters
file=open(name+"_nodes.csv", "w")
couplesappearances=dict()
for s in finalcharacters:
file.write(";")
file.write(s)
file.write("\n")
for s in finalcharacters:
newlist=[]
for f in finalcharacters:
newlist.append(0)
couplesappearances[f+"_"+s]=0
j=0
for f in finalcharacters:
for p in scenes:
if f in scenes[p] and s in scenes[p] and f!=s and finalcharacters.index(f)<finalcharacters.index(s):
long=len(magnolia[magnolia["scene"]==p])
newlist[j]=newlist[j]+long
couplesappearances[f+"_"+s]=couplesappearances[f+"_"+s]+long
j=j+1
file.write(s)
for f in newlist:
file.write(";")
file.write(str(f))
file.write("\n")
file.close()
a=pd.DataFrame(couplesappearances, index=[i for i in range(len(couplesappearances))])
finalcouples=[]
for s in pd.DataFrame(a.transpose()[0].sort_values(0, ascending=False))[0:4].iterrows():
finalcouples.append(s[0])
file=open(name+"_finalcharacters.csv", "w")
for s in finalcharacters:
file.write(s+"\n")
file.close()
file=open(name+"_finalcouples.csv", "w")
for s in finalcouples:
file.write(s+"\n")
file.close()
importantchars=[]
for char in appearances:
if appearances[char]>10:
importantchars.append(char)
file=open(name+"_sentiment_overtime_individual.csv", "w")
file2=open(name+"_sentiment_overtime_individualminsmaxs.csv", "w")
for k in finalcharacters:
print(k)
dd=getdialogue(magnolia, k, k, scenes)
dd=[str(d) for d in dd]
polarities, subjectivities=getsentiment(dd)
%matplotlib inline
import matplotlib.pyplot as plt
moveda=maverage(polarities, dd, .99)
plt.plot(moveda)
i=0
for s in moveda:
file.write(k+","+str(float(i)/len(moveda))+", "+str(s)+"\n")
i=i+1
plt.ylabel('polarities')
plt.show()
file2.write(k+"| MIN| "+dd[moveda.index(np.min(moveda))]+"\n")
file2.write(k+"| MAX| "+dd[moveda.index(np.max(moveda))]+"\n")
print("MIN: "+dd[moveda.index(np.min(moveda))])
print("\n")
print("MAX: "+dd[moveda.index(np.max(moveda))])
file.close()
file2.close()
file=open(name+"_sentiment_overtime_couples.csv", "w")
file2=open(name+"_sentiment_overtime_couplesminsmaxs.csv", "w")
for k in finalcouples:
print(k)
liston=k.split("_")
dd=getdialogue(magnolia, liston[0], liston[1], scenes)
dd=[str(d) for d in dd]
polarities, subjectivities=getsentiment(dd)
%matplotlib inline
import matplotlib.pyplot as plt
moveda=maverage(polarities, dd, .99)
plt.plot(moveda)
i=0
for s in moveda:
file.write(k+","+str(float(i)/len(moveda))+", "+str(s)+"\n")
i=i+1
plt.ylabel('polarities')
plt.show()
file2.write(k+"| MIN| "+dd[moveda.index(np.min(moveda))]+"\n")
file2.write(k+"| MAX| "+dd[moveda.index(np.max(moveda))]+"\n")
print("MIN: "+dd[moveda.index(np.min(moveda))])
print("\n")
print("MAX: "+dd[moveda.index(np.max(moveda))])
file.close()
file2.close()
for key, val in scenes.items():
for s in scenes[key]:
new="INSCENE_"+scenes[key][0]
scenes[key].remove(scenes[key][0])
scenes[key].append(new)
magnolia.dropna(subset=['dialogue'])
1
baskets=[]
spchars=["\"", "'", ".", ",", "-"]
attributes=["?", "!"]
for s in magnolia.iterrows():
if type(s[1]['dialogue'])!=float and len(s[1]['dialogue'])>0:
new=[]
for k in scenes[s[1]['scene']]:
new.append(k)
new.append("SPEAKING_"+s[1]['char'])
for k in s[1]['dialogue'].split(" "):
ko=k
for t in spchars:
ko=ko.replace(t, "")
for t in attributes:
if ko.find(t)>=0:
new.append(t)
ko=ko.replace(t, "")
if len(ko)>0:
new.append(ko.lower())
new=list(set(new))
baskets.append(new)
baskets2=[]
basketslist=[]
for k in baskets:
new=dict()
new2=[]
for t in k:
if t not in stopwords:
new[t]=1
new2.append(t)
baskets2.append(new)
basketslist.append(new2)
baskets2=pd.DataFrame(baskets2)
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
baskets2=baskets2.fillna(0)
baskets2.to_csv(name+'_basket.csv')
frequent_itemsets = apriori(baskets2, min_support=5/len(baskets2), use_colnames=True)
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1)
rules['one_lower']=[int(alllower(i) or alllower(j)) for i, j in zip(rules['antecedants'], rules['consequents'])]
rules['both_lower']=[int(alllower(i) and alllower(j)) for i, j in zip(rules['antecedants'], rules['consequents'])]
rules.to_csv(name+'_rules.csv', index=None)
| . |
|---|
| ANATOLY |
| Palabras Distintas |
|---|
| 2036 |
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 5.169173 | 11.1% |
| sentiment | Porcentaje |
|---|---|
| positive | 56.2% |
| negative | 43.8% |
| sentiment | Porcentaje |
|---|---|
| positive | 20.7% |
| negative | 15.0% |
| anticipation | 12.3% |
| trust | 12.3% |
| joy | 10.5% |
| sadness | 6.8% |
| fear | 6.6% |
| surprise | 5.5% |
| anger | 5.3% |
| disgust | 5.0% |
| sentiment | Porcentaje |
|---|---|
| negative | 37.8% |
| positive | 30.1% |
| uncertainty | 25.7% |
| litigious | 4.7% |
| constraining | 1.7% |
[1] “Analisis de Sentimientos del Personaje: ROSE” [1] “Numero total de Palabras Unicas en el texto: 696”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 5.181319 | 13.1% |
| sentiment | Porcentaje |
|---|---|
| positive | 52.98% |
| negative | 47.02% |
| sentiment | Porcentaje |
|---|---|
| positive | 19.9% |
| negative | 17.2% |
| trust | 12.1% |
| anticipation | 11.6% |
| joy | 10.1% |
| sadness | 8.3% |
| fear | 6.7% |
| anger | 4.9% |
| surprise | 4.7% |
| disgust | 4.5% |
| sentiment | Porcentaje |
|---|---|
| negative | 44.1% |
| uncertainty | 32.2% |
| positive | 18.6% |
| litigious | 3.4% |
| constraining | 1.7% |
[1] “Analisis de Sentimientos del Personaje: JACK” [1] “Numero total de Palabras Unicas en el texto: 643”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 5.323741 | 10.6% |
| sentiment | Porcentaje |
|---|---|
| positive | 61.7% |
| negative | 38.3% |
| sentiment | Porcentaje |
|---|---|
| positive | 17.3% |
| negative | 15.1% |
| anticipation | 12.0% |
| trust | 12.0% |
| joy | 10.9% |
| fear | 8.7% |
| anger | 7.3% |
| sadness | 6.7% |
| disgust | 5.0% |
| surprise | 5.0% |
| sentiment | Porcentaje |
|---|---|
| positive | 47.6% |
| negative | 26.2% |
| uncertainty | 23.8% |
| litigious | 2.4% |
[1] “Analisis de Sentimientos del Personaje: CAL” [1] “Numero total de Palabras Unicas en el texto: 424”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 5.282051 | 13% |
| sentiment | Porcentaje |
|---|---|
| positive | 56.4% |
| negative | 43.6% |
| sentiment | Porcentaje |
|---|---|
| positive | 21.7% |
| negative | 14.0% |
| joy | 12.7% |
| trust | 12.3% |
| anticipation | 12.0% |
| disgust | 7.0% |
| anger | 5.3% |
| sadness | 5.3% |
| surprise | 5.0% |
| fear | 4.7% |
| sentiment | Porcentaje |
|---|---|
| positive | 37.2% |
| uncertainty | 34.9% |
| negative | 20.9% |
| litigious | 4.7% |
| constraining | 2.3% |
[1] “Analisis de Sentimientos del Personaje: LOVETT” [1] “Numero total de Palabras Unicas en el texto: 372”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 5.555556 | 8.6% |
| sentiment | Porcentaje |
|---|---|
| positive | 66% |
| negative | 34% |
| sentiment | Porcentaje |
|---|---|
| positive | 26.3% |
| joy | 17.0% |
| anticipation | 14.6% |
| trust | 13.5% |
| negative | 9.4% |
| sadness | 6.4% |
| fear | 5.3% |
| anger | 2.9% |
| surprise | 2.9% |
| disgust | 1.8% |
| sentiment | Porcentaje |
|---|---|
| positive | 37.5% |
| negative | 31.2% |
| uncertainty | 18.8% |
| litigious | 12.5% |
[1] “Analisis de Sentimientos del Personaje: RUTH” [1] “Numero total de Palabras Unicas en el texto: 214”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 5.5 | 7.48% |
| sentiment | Porcentaje |
|---|---|
| negative | 50% |
| positive | 50% |
| sentiment | Porcentaje |
|---|---|
| positive | 16.7% |
| negative | 15.4% |
| anticipation | 14.1% |
| trust | 12.8% |
| joy | 9.0% |
| fear | 7.7% |
| sadness | 7.7% |
| disgust | 6.4% |
| surprise | 6.4% |
| anger | 3.8% |
| sentiment | Porcentaje |
|---|---|
| positive | 35.7% |
| negative | 28.6% |
| uncertainty | 21.4% |
| constraining | 7.1% |
| litigious | 7.1% |
[1] “Analisis de Sentimientos del Personaje: BODINE” [1] “Numero total de Palabras Unicas en el texto: 288”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 4.466667 | 7.29% |
| sentiment | Porcentaje |
|---|---|
| negative | 53.66% |
| positive | 46.34% |
| sentiment | Porcentaje |
|---|---|
| negative | 26.7% |
| positive | 18.7% |
| anticipation | 8.0% |
| disgust | 8.0% |
| sadness | 8.0% |
| trust | 8.0% |
| fear | 6.7% |
| joy | 6.7% |
| anger | 5.3% |
| surprise | 4.0% |
| sentiment | Porcentaje |
|---|---|
| negative | 36.4% |
| uncertainty | 36.4% |
| positive | 18.2% |
| constraining | 9.1% |
[1] “Analisis de Sentimientos del Personaje: MOLLY” [1] “Numero total de Palabras Unicas en el texto: 228”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 5.647059 | 5.7% |
| sentiment | Porcentaje |
|---|---|
| positive | 71.4% |
| negative | 28.6% |
| sentiment | Porcentaje |
|---|---|
| positive | 20.8% |
| negative | 15.1% |
| trust | 13.2% |
| anger | 11.3% |
| anticipation | 11.3% |
| surprise | 9.4% |
| disgust | 7.5% |
| joy | 7.5% |
| fear | 1.9% |
| sadness | 1.9% |
| sentiment | Porcentaje |
|---|---|
| negative | 40% |
| positive | 40% |
| constraining | 20% |
[1] “Analisis de Sentimientos del Personaje: SMITH” [1] “Numero total de Palabras Unicas en el texto: 140”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 4.933333 | 10% |
| sentiment | Porcentaje |
|---|---|
| positive | 60% |
| negative | 40% |
| sentiment | Porcentaje |
|---|---|
| anticipation | 22.5% |
| positive | 17.5% |
| negative | 12.5% |
| trust | 12.5% |
| fear | 10.0% |
| joy | 7.5% |
| sadness | 7.5% |
| surprise | 5.0% |
| anger | 2.5% |
| disgust | 2.5% |
| sentiment | Porcentaje |
|---|---|
| negative | 42.9% |
| uncertainty | 42.9% |
| positive | 14.3% |
[1] “Analisis de Sentimientos del Personaje: ANDREWS” [1] “Numero total de Palabras Unicas en el texto: 227”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 5.916667 | 9.25% |
| sentiment | Porcentaje |
|---|---|
| positive | 70.8% |
| negative | 29.2% |
| sentiment | Porcentaje |
|---|---|
| positive | 23.1% |
| anticipation | 17.9% |
| trust | 15.4% |
| joy | 11.5% |
| negative | 10.3% |
| surprise | 9.0% |
| fear | 3.8% |
| sadness | 3.8% |
| anger | 2.6% |
| disgust | 2.6% |
| sentiment | Porcentaje |
|---|---|
| negative | 46.2% |
| positive | 38.5% |
| litigious | 7.7% |
| uncertainty | 7.7% |
[1] “Analisis de Sentimientos del Personaje: LOVEJOY” [1] “Numero total de Palabras Unicas en el texto: 148”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 5.625 | 4.73% |
| sentiment | Porcentaje |
|---|---|
| positive | 55.6% |
| negative | 44.4% |
| sentiment | Porcentaje |
|---|---|
| positive | 35.7% |
| joy | 17.9% |
| anticipation | 14.3% |
| trust | 14.3% |
| surprise | 7.1% |
| anger | 3.6% |
| fear | 3.6% |
| negative | 3.6% |
| sentiment | Porcentaje |
|---|---|
| uncertainty | 66.7% |
| negative | 22.2% |
| positive | 11.1% |
[1] “Analisis de Sentimientos del Personaje: LIGHTOLLER” [1] “Numero total de Palabras Unicas en el texto: 77”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 5.555556 | 7.79% |
| sentiment | Porcentaje |
|---|---|
| positive | 66.7% |
| negative | 33.3% |
| sentiment | Porcentaje |
|---|---|
| positive | 25.0% |
| trust | 21.4% |
| negative | 14.3% |
| sadness | 14.3% |
| anticipation | 10.7% |
| disgust | 3.6% |
| fear | 3.6% |
| joy | 3.6% |
| surprise | 3.6% |
| sentiment | Porcentaje |
|---|---|
| negative | 50% |
| positive | 50% |
[1] “Analisis de Sentimientos del Personaje: FABRIZIO” [1] “Numero total de Palabras Unicas en el texto: 87”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 5.5 | 8.05% |
| sentiment | Porcentaje |
|---|---|
| positive | 75% |
| negative | 25% |
| sentiment | Porcentaje |
|---|---|
| positive | 20% |
| negative | 16% |
| surprise | 16% |
| joy | 12% |
| anger | 8% |
| anticipation | 8% |
| trust | 8% |
| disgust | 4% |
| fear | 4% |
| sadness | 4% |
| sentiment | Porcentaje |
|---|---|
| negative | 100% |
[1] “Analisis de Sentimientos del Personaje: ISMAY” [1] “Numero total de Palabras Unicas en el texto: 136”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 5.769231 | 8.82% |
| sentiment | Porcentaje |
|---|---|
| positive | 69.2% |
| negative | 30.8% |
| sentiment | Porcentaje |
|---|---|
| positive | 18.6% |
| anticipation | 16.3% |
| negative | 14.0% |
| sadness | 11.6% |
| surprise | 11.6% |
| joy | 9.3% |
| fear | 7.0% |
| disgust | 4.7% |
| trust | 4.7% |
| anger | 2.3% |
| sentiment | Porcentaje |
|---|---|
| negative | 42.9% |
| positive | 42.9% |
| uncertainty | 14.3% |
[1] “Analisis de Sentimientos del Personaje: LIZZY” [1] “Numero total de Palabras Unicas en el texto: 93”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 5.4 | 5.38% |
| sentiment | Porcentaje |
|---|---|
| negative | 50% |
| positive | 50% |
| sentiment | Porcentaje |
|---|---|
| positive | 30.8% |
| anticipation | 23.1% |
| negative | 15.4% |
| trust | 15.4% |
| fear | 7.7% |
| joy | 7.7% |
| sentiment | Porcentaje |
|---|---|
| uncertainty | 100% |
[1] “Analisis de Sentimientos del Personaje: MURDOCH” [1] “Numero total de Palabras Unicas en el texto: 73”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 3.857143 | 6.85% |
| sentiment | Porcentaje |
|---|---|
| negative | 100% |
| sentiment | Porcentaje |
|---|---|
| negative | 26.7% |
| anger | 20.0% |
| positive | 20.0% |
| trust | 13.3% |
| anticipation | 6.7% |
| disgust | 6.7% |
| fear | 6.7% |
| sentiment | Porcentaje |
|---|---|
| negative | 100% |
[1] “Analisis de Sentimientos del Personaje: STEWARD” [1] “Numero total de Palabras Unicas en el texto: 84”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 3.9375 | 15.5% |
| sentiment | Porcentaje |
|---|---|
| negative | 71.4% |
| positive | 28.6% |
| sentiment | Porcentaje |
|---|---|
| positive | 24.3% |
| anticipation | 16.2% |
| trust | 16.2% |
| joy | 10.8% |
| negative | 10.8% |
| fear | 8.1% |
| sadness | 5.4% |
| anger | 2.7% |
| disgust | 2.7% |
| surprise | 2.7% |
| sentiment | Porcentaje |
|---|---|
| negative | 75.0% |
| positive | 12.5% |
| uncertainty | 12.5% |
[1] “Analisis de Sentimientos del Personaje: TOMMY” [1] “Numero total de Palabras Unicas en el texto: 102”
| Descripcion | Score | % Founded Words |
|---|---|---|
| Entre 0 (negativo) y 10 (positivo) | 4.421053 | 14.7% |
| sentiment | Porcentaje |
|---|---|
| positive | 53.85% |
| negative | 46.15% |
| sentiment | Porcentaje |
|---|---|
| negative | 25.0% |
| positive | 12.5% |
| sadness | 12.5% |
| fear | 10.0% |
| joy | 10.0% |
| disgust | 7.5% |
| surprise | 7.5% |
| anger | 5.0% |
| anticipation | 5.0% |
| trust | 5.0% |
| sentiment | Porcentaje |
|---|---|
| negative | 33.3% |
| positive | 33.3% |
| uncertainty | 33.3% |
| Personaje | Min_Max | Dialogo |
|---|---|---|
| ROSE | MIN | Jack, this is impossible. I can’t see you. |
| ROSE | MAX | Titanic was called the Ship of Dreams. And it was. It really was… |
| JACK | MIN | I just seem to spew ’em out. Besides, they’re not worth a damn anyway. |
| JACK | MAX | It brought me to you. And I’m thankful, Rose. I’m thankful. |
| CAL | MIN | God damn it to hell! Come on. |
| CAL | MAX | Probably best. It’ll be all business and politics, that sort of thing. Wouldn’t interest you. Good of you to come. |
| LOVETT | MIN | Would you like anything? |
| LOVETT | MAX | Just let me hold it in my hand, Rose. Please. Just once. |
| RUTH | MIN | Will the lifeboats be seated according to class? I hope they’re not too crowded – |
| RUTH | MAX | Rose, this is not a game! Our situation is precarious. You know the money’s gone! |
| MOLLY | MIN | Come on, you heard the man. Get in the boat, sister. |
| MOLLY | MAX | Well, Jack, it sounds like you’re a good man to have around in a sticky spot – |
| BODINE | MIN | Ooohh daddy-oh, are you seein’ what I’m seein’? |
| BODINE | MAX | We’ve put together the world’s largest database on the Titanic. Okay, here… |
| SMITH | MIN | Can you shore up? |
| SMITH | MAX | No, but we’re making excellent time. |
| ANDREWS | MIN | Anyone in here? |
| ANDREWS | MAX | Sleep soundly, young Rose. I have built you a good ship, strong and true. She’s all the lifeboat you need. |
| LOVEJOY | MIN | I’ve found her. She’s just over on the port side. With him. |
| LOVEJOY | MAX | Miss Rose? Hello? |
| Parejas | Min_Max | Dialogo |
|---|---|---|
| ROSE_JACK | MIN | And these were drawn from life? |
| ROSE_JACK | MAX | Right. |
| ROSE_CAL | MIN | I’m not going without you. |
| ROSE_CAL | MAX | There’s the Countess Rothes. And that’s John Jacob Astor… the richest man on the ship. His little wifey there, Madeleine, is my age and in a delicate condition. See how she’s trying to hide it. Quite the scandal. And over there, that’s Sir Cosmo and Lucile, Lady Duff-Gordon. She designs naughty lingerie, among her many talents. Very popular with the royals. |
| ROSE_RUTH | MIN | You unimaginable bastard. |
| ROSE_RUTH | MAX | I don’t understand you. It is a fine match with Hockley, and it will insure our survival. |
| CAL_RUTH | MIN | Will the lifeboats be seated according to class? I hope they’re not too crowded – |
| CAL_RUTH | MAX | A real man makes his own luck, Archie. |
## [1] "Lift Promedio de las Reglas de Asociacion: 31.097354302274"
## [1] "Desviación estandar del Lift de las Reglas de Asociacion: 14.3179926328554"
## [1] "Deciles del Lift : "
## 10% 20% 30% 40% 50% 60%
## 9.075472 14.800000 21.863636 29.875776 41.826087 41.826087
## 70% 80% 90% 100%
## 41.826087 41.826087 41.826087 192.400000
| Numero de Dialogos | Lift Minimo | Lift Maximo |
|---|---|---|
| 50,670 | -3 | 3 |
| 101,482 | 3 | 10 |
| 138,260 | 10 | 16 |
| 112,646 | 16 | 23 |
| 112,166 | 23 | 30 |
| 52,868 | 30 | 36 |
## [1] "Leverage Promedio de las Reglas de Asociacion: 0.00799696733196904"
## [1] "Desviación estandar del Leverage de las Reglas de Asociacion: 0.0060371080556565"
## [1] "Deciles del Leverage : "
## 10% 20% 30% 40% 50% 60%
## 0.004846322 0.005003004 0.005073241 0.005073241 0.005073241 0.006605478
## 70% 80% 90% 100%
## 0.007102537 0.007102537 0.022740436 0.105869183
| Numero de Dialogos | Leverage Minimo | Leverage Maximo |
|---|---|---|
| 10,778 | -0.0018 | 0.0018 |
| 730,768 | 0.0018 | 0.0055 |
| 348,438 | 0.0055 | 0.0091 |
| 17,900 | 0.0091 | 0.013 |
| 15,232 | 0.013 | 0.016 |
| 7,210 | 0.016 | 0.02 |
Pagerank: Magnolia.